SAR image despeckling with a multilayer perceptron neural network

Tang, Xiao; Zhang, Lei; Ding, Xiaoli

doi:10.1080/17538947.2018.1447032

Cited by 42 publications

(44 citation statements)

References 38 publications

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“…The choice for this class is based on the characteristics of the data (non-linearly separable) and ease of implementation. Furthermore, perceptrons and MLPs have already been applied to SAR images with different objectives: discrimination of different types of targets (vehicles) [14], [15] and noise removal [16]. Given an MLP used for binary classification, the i-th layer of the network (output neuron) can be formulated as…”

Section: B Discrimination Algorithm (Mlp)mentioning

confidence: 99%

Discrimination Algorithm for False Alarm Reduction in SAR Incoherent Change Detection

Campos¹,

Molin²,

Pettersson³

et al. 2019

Anais De XXXVII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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This paper introduces an additional stage for incoherent change detection algorithms (CDAs) based on multilayer perceptron (MLP). Pixels the CDA initially assigned as detections are re-evaluated by the MLP based on features extracted from the processed images, according to a previously performed training. The tests considered multitemporal synthetic aperture radar (SAR) images from the CARABAS-II system, in a scenario where military vehicles were concealed under vegetation. Preliminary results show that the proposed method can reduce the false alarm rate (FAR) by up to 73% for the same probability of detection.

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Section: B Discrimination Algorithm (Mlp)mentioning

confidence: 99%

Discrimination Algorithm for False Alarm Reduction in SAR Incoherent Change Detection

Campos¹,

Molin²,

Pettersson³

et al. 2019

Anais De XXXVII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

View full text Add to dashboard Cite

show abstract

“…In order to function the NN, the weights are being initialized. As a result, the network is made to learn by using some learning methods and rules [2][3][4][5][6][7][8][9][10][11][12][13][14][15]. The connection weights are adjusted during the training.…”

Section: Introductionmentioning

confidence: 99%

An efficient learning algorithm for periodic perceptron to test XOR function and parity problem

et al. 2020

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Artificial neural network (ANN) is an important tool, which is used in numerous fields, such as computer vision, pattern recognition, signal processing, solving optimization problems, and voice analysis and synthesis. Many real-life problems, where the future events play a vital role, are based on the past history. For example, predicting the behavior of stock market indices and electrical load forecasting. In this paper, we establish an efficient learning algorithm for periodic perceptron (PP) in order to test in realistic problems, such as the XOR function and the parity problem. Here, the periodic threshold output function guarantees the convergence of the learning algorithm for the multilayer perceptron. By using the binary Boolean function and the PP in single and multilayer perceptron, XOR problem is solved. The performance of PP is compared with multilayer perceptron and the result shows superiority of PP over the multilayer perceptron.

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“…Recently, with the advent of non-local means filtering (NLM) [15] for reducing the additive Gaussian noise, this idea was extended to suppress the speckle from SAR images [16]- [25], as well as Polarimetric SAR image despeckling [26], [27]. Besides these non-local despeckling approaches, some despeckling methods based on neural networks [28]- [30] and total variation [31], [32] were also presented in the literature.…”

Section: Introductionmentioning

confidence: 99%

Hybrid SAR Speckle Reduction Using Complex Wavelet Shrinkage and Non-Local PCA-Based Filtering

Farhadiani

Homayouni

Safari

2019

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this paper, a new hybrid despeckling method, based on Undecimated Dual-Tree Complex Wavelet Transform (UDT-CWT) using maximum a posteriori (MAP) estimator and non-local Principal Component Analysis (PCA)based filtering with local pixel grouping (LPG-PCA), was proposed. To achieve a heterogeneous-adaptive speckle reduction, SAR image is classified into three classes of point targets, details, or homogeneous areas. The despeckling is done for each pixel based on its class of information. Logarithm transform was applied to the SAR image to convert the multiplicative speckle into additive noise. Our proposed method contains two principal steps. In the first step, denoising was done in the complex wavelet domain via MAP estimator. After performing UDT-CWT, the noise-free complex wavelet coefficients of the log-transformed SAR image were modeled as a two-state Gaussian mixture model. Furthermore, the additive noise in the complex wavelet domain was considered as a zeromean Gaussian distribution. In the second step, after applying inverse UDT-CWT, an iterative LPG-PCA method was used to smooth the homogeneous areas and enhance the details. The proposed method was compared with some state-of the-art despeckling methods. The experimental results showed that the proposed method leads to a better speckle reduction in homogeneous areas while preserving details. Index Terms-Gaussian mixture model, homomorphic transformation, non-local filtering, undecimated dual-tree complex wavelet transform. I.

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SAR image despeckling with a multilayer perceptron neural network

Cited by 42 publications

References 38 publications

Discrimination Algorithm for False Alarm Reduction in SAR Incoherent Change Detection

Discrimination Algorithm for False Alarm Reduction in SAR Incoherent Change Detection

An efficient learning algorithm for periodic perceptron to test XOR function and parity problem

Hybrid SAR Speckle Reduction Using Complex Wavelet Shrinkage and Non-Local PCA-Based Filtering

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